IT Management in Data Science and Machine Learning

  • November 9 - May 31
  • 2 hours per week
  • 240Br
  • Minsk, B. Chmialnickaha 7
IT Management in Data Science and Machine Learning

Learn new global business trends, networking, strategy, project management with appliance to data science, machine learning and algorithms. The program is an introduction to principles of machine learning and practical solutions using predictive analytics. Mathematics & Statistics are the founding steps for Data Science and Machine learning. Most of the successful data scientists come from one of these areas – computer science, applied mathematics & statistics or economics. If you wish to excel in data science, you must have a good understanding of basic algebra and statistics.


For Whom

  • Professionals
  • Students


What You Learn and Develop

  • Leadership
  • Teamwork
  • Learning to learn
  • Goal setting
  • Decision making
  • Idea generation
  • Design thinking
  • Creative, analytical, strategic, critical, structured thinking
  • Negotiation and communication tools

How You Learn

Benefit from ENGO natural learning methods included in the program:

  • Systematic learning
  • Live projects teamwork Agile and Scrum-like
  • Action learning
  • Design thinking
  • Case-learning
  • Business games
  • Visualization
  • Edutainment
  • Non-sitting approach

What You Get

  • ENGO community of stellar experienced entrepreneurs and professionals with distinctive technology vision, their reference and hands-on assistance for career development and your projects' traction
  • Online access to ENGO resources for collaboration and results
  • Personal online portfolio on ENGO official website with track record of completed projects during the program, for career development

Entry Requirements for Learners

  • English: should be sufficient to express your thoughts
  • You have no experience in Data Science, Machine Learning and Math Statistics.

Program Curriculum

  • Software development methodologies overview and examples.
  • Planning stages and tools.
  • Scope. Scope baseline. Requirements categories.
  • Cost. Estimation methods and types.
  • Quality. Quality criteria, control and cost.
  • Team. Roles and responsibilities.
  • Analytics. Earned value management.
  • Risks. Value and analysis.
  • Presentation tools and techniques.
  • Visualization techniques.
  • Introduction to machine learning.
  • Machine learning relation to statistics and data analysis.
  • Computer algorithms usage for data patterns searching.
  • Data patterns usage in making decisions and predictions on real-world examples from healthcare and other industries.
  • Uncovering hidden themes in large collections of documents using topic modeling.
  • Data preparation.
  • Dealing with missing data.
  • Creating custom data analysis solutions for different industries.
  • Basic algorithmic techniques and dynamic programming.
  • Data science maths skills.
  • Introduction to descriptive statistics.
  • Introduction to inferential statistics.
  • Introduction to probability and data.
  • Applications of finite math.
  • Probability: basic concepts and discrete random variables.
  • Mathematical biostatistics.
  • Applications of linear algebra.
  • Introduction to mathematical thinking.
  • Agile Scrum definition and values.
  • Scrum team roles and responsibilities.
  • Scrum events.
  • Scrum artifacts
  • Scrum analytics and project tracking.

Program Tutors

Roman Gromov
Roman Gromov

ENGO Cofounder.

Head of ENGO Blockchain Lab.

CEO at IG Dev Belarus and XP(Capital). Head of IT Development at Cyber Security Research and Development Center in Minsk.

20 years of successful career in IT industry. Graduated from BSU Law department.

Anna Maltseva
Anna Maltseva

ENGO Cofounder.

IT Corporate Business Processes Professional.

IT Project Manager for strategic and commercial projects. Mentor.

11 years of successful career in IT industry.

Author of corporate training programs for managers and team leaders. Graduated from BSU Mechanics and Mathematics.

What People Say